Category
4,009 papers
This paper proposes ReG-SAM, a SAM-based framework tailored to 2D vessel segmentation that leverages reference graph set for enhancing vascular representations and introduces two modality-aware representations derived from the reference masks.
The results provide exploratory cross-dataset evidence for stain-aware, parameter-efficient federation; they do not establish formal privacy, patient-level independence, prospective deployment, or clinical validation.
Pocket-STVG is presented, a lightweight cascade architecture that addresses STVG by combining efficient pre-trained components instead of large end-to-end models and establishing a favorable performance-efficiency trade-off for STVG.
A 4B model that uses its own dense metric predictions as object-grounded evidence for language generation and leads the evaluated methods in instance-level metric depth estimation and overall accuracy on both relative and metric reasoning tracks, while broadly preserving general VQA performance and improving spatial un...
Reach audiences
Advertise in front of researchers, engineers, and readers.
A scalable hybrid Quantum Diffusion Model is presented, based on a Discrete-Time Quantum Walk algorithm, executed on a real quantum device, to model the forward dynamics of the diffusion model, and its use for medical image analysis is evaluated.
The exact best-case limit for any image-only verifier is given: the largest robust target-acceptance gap equals the minimum total-variation distance between the target distribution and the set of attacked source distributions.
FLIP is a validation step for intervention-based mechanistic interpretability, not a steering method, for testing whether a logit-facing intervention site in an open-weight vision-language model (VLM) supports structured, task-linked computation rather than generic perturbation.
Across generator swaps, including substitutions with similar model versions and model variants, FARE is effective at detecting swaps, consistently outperforming existing baselines at strict operating points, and remains effective under the exact-model and decision-only attacks evaluated in this work.
Preliminary evidence is presented that reinforcement learning with verifiable rewards can elicit some latent multi-view competence in the base model, pointing to training-time approaches as a promising direction for future work.
OneWorld, a shared-mechanism counterfactual generation framework that jointly models multiple action-conditioned futures under a common latent physical mechanism, is proposed and experiments show that OneWorld improves cross-intervention physical consistency while maintaining competitive single-rollout prediction quali...
Spackle is a lightweight residual learning framework that mitigates capacity competition without sacrificing efficiency on large-view-deviation NVS cases and achieves state-of-the-art performance on large-view-deviation cases.
This work proposes UltraG-Agent, which combines the semantic reasoning capabilities of a VLM with the ultrasound-specific segmentation capability of UltraSAM3, and shows that UltraG-Agent substantially improves both semantic prediction and pixel-level visual grounding.
Professor Paris Smaragdis develops computers with superhuman hearing for applications like speech recognition, music understanding, and environmental monitoring.
GPT-Lab
· gpt-lab.eu
Oct 8, 2026
Exploring how generative AI could make machine vision more accessible to businesses. The post GenEye in a Box: Making Machine Vision Something You Can Just Ask For appeared first on GPT-Lab.
Jennifer Neville did not want to go into computer science—but that’s exactly where she landed. Neville discusses the starts and stops that led to her professional sweet spot and her work identifying “surprising failures” making it hard for AI to handle complexity. The post What AI gets wrong and what failure teaches us appeared first on Microsoft Research.
A Blog post by H company on Hugging Face
A Blog post by Liquid AI on Hugging Face
Computer scientist, entrepreneur, and philanthropist will collaborate with the MIT Schwarzman College of Computing to advance AI and scientific discovery.
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.
